Example #1
0
def _test_ridge_classifiers(filter_):
    for clf in (RidgeClassifier(), RidgeClassifierCV()):
        clf.fit(filter_(X_iris), y_iris)
        y_pred = clf.predict(filter_(X_iris))
        assert np.mean(y_iris == y_pred) >= 0.8

    clf = RidgeClassifierCV()
    n_samples = X_iris.shape[0]
    cv = KFold(n_samples, 5)
    clf.fit(filter_(X_iris), y_iris, cv=cv)
    y_pred = clf.predict(filter_(X_iris))
    assert np.mean(y_iris == y_pred) >= 0.8
Example #2
0
def _test_ridge_classifiers(filter_):
    n_classes = np.unique(y_iris).shape[0]
    n_features = X_iris.shape[1]
    for clf in (RidgeClassifier(), RidgeClassifierCV()):
        clf.fit(filter_(X_iris), y_iris)
        assert_equal(clf.coef_.shape, (n_classes, n_features))
        y_pred = clf.predict(filter_(X_iris))
        assert np.mean(y_iris == y_pred) >= 0.8

    clf = RidgeClassifierCV()
    n_samples = X_iris.shape[0]
    cv = KFold(n_samples, 5)
    clf.fit(filter_(X_iris), y_iris, cv=cv)
    y_pred = clf.predict(filter_(X_iris))
    assert np.mean(y_iris == y_pred) >= 0.8
Example #3
0
def _test_ridge_classifiers(filter_):
    for clf in (RidgeClassifier(), RidgeClassifierCV()):
        clf.fit(filter_(X_iris), y_iris)
        y_pred = clf.predict(filter_(X_iris))
        assert np.mean(y_iris == y_pred) >= 0.8

    clf = RidgeClassifierCV()
    n_samples = X_iris.shape[0]
    cv = KFold(n_samples, 5)
    clf.fit(filter_(X_iris), y_iris, cv=cv)
    y_pred = clf.predict(filter_(X_iris))
    assert np.mean(y_iris == y_pred) >= 0.8